The problem of face detection has received a great deal of attention. Most conventional techniques concentrate on face recognition, assuming that a region of an image containing a single face has already been detected and extracted and will be provided as an input. Common face detection methods include: knowledge-based methods; feature-invariant approaches, including the identification of facial features, texture and skin color; template matching methods, both fixed and deformable; and appearance based methods.
After faces are detected, there is a need to categorize the detected face images of each individual into a group regardless whether the identity of the individual is known or not. For example, if two individuals Person A and Person B are detected in ten images. Each of the images can be categorized or tagged one of the four types: A only; B only, A and B; or neither A nor B. Algorithmically, the tagging of face images require training based one face images or face models or known persons, for example, the face images of family members or friends of a user who uploaded the images.
There is still a need for more convenient and more accurate methods to separately tag or categorize face images of different persons.